Should future interventional neuroradiologists be screened for mutations that impair radiation-induced DNA repair?
Bibliographic record
Abstract
In our current medical practice, an increasing number of specialists now have access to radiology technical platforms in order to perform imaging-guided procedures. Although knowledge about the current guidelines and radiation protection devices is a pre-requisite for the use of radiation, the preventive measures are often more or less strictly followed, leading to chronic daily exposure to significant doses of radiation and large accumulated lifetime exposures. Aortic intervention, electrophysiology, and neuro intervention in particular can result in large doses to the operators. Interventionalists might try to rationalize their dismissal of the exposure risks with various excuses: they don't know where they left their badges (even though, guiltily, they would readily admit it is good practice to always wear them), the estimated short duration of the procedure, significant muscular strain and spasm caused by the heaviness of lead aprons, decreased dexterity with lead gloves, or discomfort in wearing lead protective glasses. But their dismissive attitude is most likely due to the inherent inability to feel threatened by something they cannot see or feel, a commitment to the patient at all cost, and a culture of bravado that reinforces their behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".